Abstract. In automotive systems,
(i) distributed, real-time algorithms, operating on a set of networked
electronic control units (ECUs), are used to control the car (for example: the
stability system); and (ii) fine grained video and audio algorithms are often
mapped to an ensemble of processors in order to achieve the required
performance at a known cost and, for untethered devices, with a known power
budget (for example: an after-market cell phone/PDA supporting video and audio
streaming – terrestrial and satellite, GPS, etc.). The mapping of complex algorithms to systems
containing multiple processors that satisfy some set of optimality conditions
is notoriously difficult [8]. An empirical, refutation-based (scientific)
process; design of experiments technology; and multi-variate statistics [6] can
be employed to help drive the optimization process. Experimentation involves
performing potentially many hundreds of experiments, each of which is meticulously
measured, so that the processed data can be used to make decisions about the
next steps to take in modifying the system (hardware, algorithms, software
mappings, input/output interfaces, etc.) in the iterative march towards an
optimal system. Undertaking such an experimental regime using physical hardware
is prohibitively expensive in time and resources. The use of virtual (modeled)
systems, together with the statistical machinery and empirical processes, makes
this process plausible and necessary. This strategy is somewhat at variance
with the AutoSAR & JASPAR standardization initiatives but is consistent
with the notion of services-based interfaces where, for example, the always
best external data sources (such as satellite and terrestrial RF data) –
whether captured through the car’s infrastructure or a plugged-in
external unit – should be available to whatever process is providing a
requested service.